Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features—CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific  Deep learning predicts cell-type-specific 3D chromatin contacts from DNA sequence, chromatin accessibility and CTCF binding. The genome organization is cell type-specific and largely determined by features in DNA sequence and trans-acting factors that regulate chromatin interactions7. Chromatin looping within TADs restricts enhancer–promoter interactions to regulate cell-type-specific gene expression9. While the general scaffold of chromatin organization is well described, revealing the mechanisms underlying cell-type-specific chromatin structure and the implications to gene expression remains challenging14. Chromatin conformation capture technologies, such as Hi-C, are typically time- and resource-consuming1, limiting their contribution to understanding how chromatin organization determines cell-type-specific gene expression.In mammalian cells, interphase chromosomes are hierarchically organized into large compartments containing topologically associating domains (TADs) at the sub-megabase scale16. DNA sequence encodes motifs that act with chromatin binding proteins to define genome folding, and thus can be used to make approximate prediction of chromatin organization20. However, due to the lack of genomic features which govern cell-type-specific interactions, these approaches are unable to make accurate de novo predictions in different cell types20. Conversely, methods that rely only on chromatin profiles lacking motif features in DNA sequence often require multiple epigenomic data to improve predictive power26. These limitations render the current methods unsuitable for high-throughput in silico investigation of cell-type-specific mechanisms of chromatin organization.Owing to its ability to model complex interactions, deep learning has emerged as a powerful approach for studying genomic features and reducing the need for experimental analyses of chromatin organizationWe propose that an accurate de novo prediction of chromatin folding requires a multimodal neural network incorporating both DNA sequence and cell-type-specific genomic features. For practicality, it should also use a minimal set of inputs without compromising performance. Based on these principles, we developed C.Origami, a deep neural network that synergistically integrates DNA sequence features and two essential cell-type-specific genomic features: CTCF binding and chromatin accessibility . Applying ISGS to T cell acute lymphoblastic leukemia (T-ALL) cells and normal T cells, we identified T-ALL-specific regulation of chromatin organization through cis-elements and cell-type-specific trans-regulators. Taken together, these results demonstrate that C.Origami can serve as a high-throughput in silico genetic perturbation platform for future studies of three-dimensional (3D) chromatin organization.The accuracy of C.Origami enables in silico genetic perturbation experiments that assess the impact of 27. The decoder transforms the processed features to make task-specific predictions. In this study, we deployed a two-dimensional (2D) convolutional neural network with a large receptive field as a decoder and named this variant model Chromatin Origami (C.Origami) for predicting chromatin organization as captured by Hi-C contact matrices , assay for transposase-accessible chromatin using sequencing (ATAC–seq) and DNA sequence features as potential inputs that can informatively contribute to predicting cell-type-specific 3D chromatin organization  window to cover typical TADs, and outputs a Hi-C contact matrix with a bin size of 8,192 base pairs (bp) Fig. 11. As poion Fig. .13, and randomly split the chromosomes into training, validation (chromosome 10) and test sets (chromosome 15)  Fig. . We foun15) Fig. . Removin15) Fig. . Ablatin15) Fig. . Last, w15) Fig. .Fig. 2C.To systematically assess C.Origami’s performance, we first calculated insulation scores to evaluate chromatin organization similarity between experimental and predicted Hi-C matrices . C.OrigaLoop calling identifies point-to-point interactions from Hi-C matrix. To further evaluate C.Origami’s performance, we performed loop calling on both prediction and experimental Hi-C in IMR-90 cells . We foun18, DeepC19 and Orca20. After preprocessing and standardizing the results from different methods  > 1) IMR-90-specific and GM12878-specific loops with 0.88 and 0.87 AUROC, respectively  translocation35  within the neo-TAD  and not in male cell lines , and an attention-based score derived from the transformer module  of input elements . Group 3 elements show a positive correlation between their impact scores and ATAC–seq signal intensity, and are highly enriched in promoter and enhancer regions, suggesting the presence of enhancer–promoter or promoter–promoter interactions  has been shown to colocalize with CTCF, acting as a potential architectural protein to organize chromatin structurents Fig. . We founnts Fig. . This obcis-regulatory elements, we performed ISGS and calculated genome-wide impact scores in CUTLL1, Jurkat and normal naive T cells  leads to H3K27ac alterations in a subset of NOTCH1-associated chromatin interactions, while inhibiting CDK7 (+THZ1) leads to widespread H3K27ac changes36. To test the hypothesis that pharmacological inhibition of CDK7 leads to broader chromatin organization changes in T-ALL, we systematically assessed the relative contribution of trans-acting factors and found that CDK7 was ranked among the top factors in regulating chromatin organization, whereas the predicted contribution of NOTCH1 was ranked much lower  and/or the 4D Nucleome Data Portal (https://data.4dnucleome.org). To minimize bias in Hi-C data preprocessing, we obtained counts data in raw fastq format. The reads from human cell lines were aligned to GRCh38 human reference genome and mouse cell lines were aligned to mm10 mouse genome. The alignments were filtered at 10-kb resolution and iteratively corrected with HiC-bench56. To ensure the compatibility of the prediction result with downstream analytical tools, we only used a reversible natural log transform to process the Hi-C prediction targets. Prediction from C.Origami with exponential transformation can be directly used as Hi-C chromatin contact matrix data for any downstream analysis.We used seven human and mouse Hi-C profiles in this study: IMR-90, GM12878, H1-hESC, K562, CUTLL1, T cell, Mouse Patski (Supplementary Table www.ncbi.nlm.nih.gov/geo) and the ENCODE data portal (www.encodeproject.org/). CUTLL1 ATAC–seq was sequenced according to a standard method57. ATAC–seq libraries were generated from 0.5 × 106 CUTLL1 cells. Libraries were sequenced on an Illumina NovaSeq using 100-bp paired-end reads. Details on accession number are listed in Supplementary Table 58. The sparse narrowPeak file was converted back to bigWig with ucscutils. We performed a log(x + 1) transformation on both dense and sparse bigWig files and used them as inputs to the model.CTCF ChIP–seq and ATAC–seq data for most cell types are publicly available online from GEO (We used the reference genome sequence (hg38 and mm10) from the UCSC Genome Browser database. The original fasta file includes four types of nucleotides and ‘n’ for unknown type. We retained the ‘n’ category and encoded it as the unknown fifth ‘nucleotide’. After encoding, each nucleotide is a five-channel one-hot vector representing ‘ATCGN’, respectively. The same reference genome sequence was used for all cell types.The training data consist of DNA sequence, CTCF-binding signal, ATAC–seq signal and Hi-C matrix from the IMR-90 cell line. The input data to the model include DNA sequence, CTCF ChIP–seq signal and ATAC–seq signal at a 2,097,152-bp region. The output target is the Hi-C matrix at the corresponding region. The Hi-C matrix was originally called at 10-kb resolution and downscaled to 8,192 bp to match the model output resolution. To generate batches of training data, we defined 2-Mb sliding windows across the genome with 40-kb steps. Windows that have overlap with telomere or centromere regions were removed. We randomly split the genome into training, validation and test chromosomes. Chromosomes 10 and 15 were used as the validation set and the test set, respectively. The rest of the chromosomes were used as the training set.C.Origami is implemented with the PyTorch framework. The model consists of two 1D convolutional encoders, a transformer module and a task-specific 2D convolutional decoder. The sequence and genomic feature encoder has five and two input channels, respectively. To reduce memory consumption, encoders start with a 1D convolution header with stride 2. To reduce the input length from 2 Mb down to 256 bins, we deployed 12 convolution modules, each of which consists of a residual block and a scaling block. The residual block has two sets of convolution layers with kernel width 5 and the same padding. Batch normalization and ReLU nonlinearity follow each convolutional layer, and the start and end positions of the residual block are connected by a residual connection. The residual blocks do not alter dimension of inputs. The residual connections within the residual block help promote information propagation. The scaling block consists of a 1D convolutional layer with kernel size 5 and stride 2 followed by batch normalization and ReLU activation. The scaling block reduces input length by a factor of 2 and increases the number of hidden layers. We increase the hidden size according to this configuration: 32, 32, 32, 32, 64, 64, 128, 128, 128, 128, 256, 256. The output from the last scaling module has a length of 256 with 256 channels.59. Specifically, we set the number of hidden layers to 256 and ReLU as the activation function, and used eight attention heads. We used relative key query as positional embedding and set the maximum length to be 256. After the transformer module, the model concatenates each position in the 256 bins to every other position to form a 256-by-256 interaction map. The concatenation function takes the 256-bin sequence from the feature extraction module and outputs a 256-by-256 grid where location  is a concatenation of the features at i and j positions. Since each bin has 256 channels, the concatenation produces a 512-channel 256-by-256 3D tensor.The transformer module is built with eight customized attention layers similar to a BERT modelThe decoder consists of five dilated residual networks. We designed the dilation at the corresponding layer to be 2, 4, 8, 16, 32 so that the receptive field of each pixel at the last layer covers the input space, reinforcing interactions between different elements. At the end of the decoder, we use a Conv2D layer with 1 × 1 kernel to combine 256 channels down to one channel, and the output is a 256 × 256 matrix with one channel. The 256 × 256 output from the model was compared with the experimental Hi-C map (ground truth) via an MSE loss. The loss was back propagated through the whole network for gradient updates.To train the model, we used a training batch size of 8 and Adam optimizer with a learning rate of 0.002. A cosine learning rate scheduler with 200-epoch period was used for stabilizing training. We used three types of data augmentations. First, we selected the 2-Mb window with random shifts within 0.36-Mb range. Second, we reverse-complemented the sequence and flipped the target Hi-C matrix with 0.5 probability. Third, we added Gaussian noise to all input signals with zero mean and 0.1 standard deviation. The model achieved minimal validation loss when trained for 54 epochs. The model training time was 18 h on a GPU cluster with quad NVIDIA Tesla V100 GPUs, 320 GB of RAM and 10 CPU cores. Model inference with a mobile NVIDIA RTX 2060 GPU can be achieved in under 1 s, and 3 s on a mobile Intel i7-8750H CPU. To run prediction in IMR-90, the reference DNA sequence, CTCF ChIP–seq and ATAC–seq from IMR-90 in a 2-Mb region are taken as input. For de novo prediction in a target cell type, we replaced IMR-90 CTCF ChIP–seq and ATAC–seq with the corresponding CTCF and ATAC–seq from the specific target while keeping the same reference sequence.56. We also added a pseudocount calculated from chromosome-wide average intensity to prevent division by zero in unmappable regions. Given that all the regions contain n interactions, the insulation score can be formulated as follows:Insulation score is implemented as the ratio of maximum left and right region average intensity and the middle region intensity61 to identify significant interactions. We used a resolution of 10 kb, and minimum and maximum distances of 30 kb and 1 Mb. For loop calling on predicted matrices, we converted the predicted matrix back to valid pairs by merging predictions to chromosomes and counting the discretized intensity value. FitHiC generated a list of significant interactions with corresponding false discovery rate (FDR)-corrected Q values using global background as reference. For loop analysis on IMR-90, we computed AUROC and overlap between loops called from experimental Hi-C and loops called from predicted Hi-C. To calculate AUROC, we used predicted loops as target. Q value cutoffs ranging from 1 × 10−5 to 1 × 10−13 are selected to filter significant loops called from the predicted Hi-C. Then, the Q values from loops called from experimental Hi-C were compared with significant loops called from prediction to calculate an AUROC. For overlap analysis, we chose a fixed 1 × 10−5 cutoff for loops called from predicted and experimental Hi-C and compared the overlap of significant loops. For loop analysis on specific types of interaction, we overlapped the two anchors of each loop and obtained the categories for each loop called. The loops were then filtered by different categories and the same AUROC and overlap analysis was performed on each category of loops.We used the Hi-C valid pairs with the FitHiC software−7 as a threshold for significant loops. Then we further categorized specific loops into IMR-90 specific or GM12878 specific according to the log2fc of loop interaction counts. To calculate AUROC, we used log2fc in place of the Q value cutoff from previous analysis. We compared two log2fc values. The first log2fc is between predicted loops in cell type 1 and predicted loops in cell type 2 . The second log2fc is between experimental loops in cell type 1 and predicted loops in cell type 2 . Then the same AUROC and overlap analysis was performed for each of the two cell-type-specific groups. For loop analysis on a specific type of interaction in a cell-type-specific way, the same anchor overlap was performed with corresponding AUROC and overlap analysis.For cell-type-specific loop analysis between IMR-90 and GM12878, we first used a more stringent cutoff of 1 × 10To bridge adjacent 2-Mb-window predictions into chromosome-wide Hi-C contact matrices, we ran the prediction in a sliding window with 262,144-bp step size, which is 1/8 of the 2-Mb prediction window. All predictions were in-painted to their corresponding location on the contact map with multiple overlaps. To correct for different levels of overlap, we counted the total times of overlap for every pixel and divided by the number of overlaps. The resulting chromosome-wide prediction can be directly used for downstream analysis such as TAD calling, loop calling and insulation score calculation.i was calculated by aggregating the line that is parallel to the Hi-C diagonal with offset of i. Stratified correlation was calculated as Pearson’s r between the shifted diagonal line of prediction and ground truth.Distance-stratified intensity and correlation calculations were based on fused chromosome prediction. Stratified intensity at distance 18, DeepC19 and Orca20. We compared the performance using four metrics: insulation score correlation, observed versus expected Hi-C metrices correlation, MSE and distance-stratified correlation. We calculated the four metrics separately for the four models by comparing their prediction to the experimental Hi-C data. The comparison was carried out in two different cell types: (1) the training cell type, IMR-90 cells, which most models were trained on and (2) a new cell type, GM12878 cells, aiming to quantify the performance of de novo prediction of chromatin organization of the four models.We compared the performance of C.Origami against three previously published methods: AkitaWe generated a set of sliding windows that covers the whole genome and can be predicted by each model. Since Akita and DeepC are only able to predict interaction within a 1-Mb window, we restricted the test regions to 1-Mb blocks. To generate a genome-wide testing dataset, we selected all 1-Mb regions in a sliding window with 0.5-Mb overlap between neighboring regions. To ensure compatibility with all models’ prediction windows, the sliding window starts and ends 1.5 Mb after chromosome starting location and before ending location to create buffer regions for models requiring 2-Mb windows as inputs. In total, 5,935 regions were generated genome-wide. We used all four models to predict the interaction for the corresponding regions.The most relevant versions of the previous models were selected for comparison. For Akita, the IMR-90 output channel was selected. For DeepC, we used their model trained with IMR-90 data. Orca was only trained on two cell types, human foreskin fibroblasts (HFFs) and H1-hESCs. We used the HFF model because HFF is also a fibroblast cell line similar to IMR-90. The comparison turned out to be valid because even though Orca was trained on HFF, it outperformed both Akita and DeepC on IMR-90 in many benchmarks. For C.Origami, we used the IMR-90-trained model.It is necessary to perform scaling and normalization to each model’s outputs due to their varied prediction target customizations. Akita predicts a 1,048,576-bp region with 512 bins. We removed the extra 48,576 bp on the sides to make the prediction 1 Mb, followed by rescaling into 128 bins. Orca can predict interactions at multiple scales. Since C.Origami used a 2-Mb window as prediction target, we selected the 2-Mb window in Orca for consistency. The prediction was then cropped to 1 Mb and rescaled to 128-by-128. For C.Origami, the prediction is a 2,097,152-bp window. We cropped the prediction to leave the center 1-Mb regions and rescaled to 128 bins.DeepC’s prediction target is different from other models, 45-degree rotations. DeepC also produces predicted Hi-C maps in different scales compared with other methods. Thus, we performed a series of transformations , they cannot be compared directly. We defined the evaluation target as logged Hi-C intensity with iterative correction and eigenvector decomposition (ICE): ). Logged intensity has a few advantages over observed versus expected map. First, it allows for computing insulation scores. Second, it can be converted to observed versus expected while the reverse is not straightforward. It can also be converted to raw counts by taking the exponent. Third, it is used as the default Hi-C format for most downstream analysis pipelines such as loop calling and visualization.The second step to make the models comparable is to normalize model outputs to the evaluation Hi-C target. Since each model used a different original prediction target, the intensities of prediction and evaluation target show a large discrepancy depending on the model. Specifically, DeepC results stood out with a unique pattern which might be a result of their custom stratified binning method  and low similarity (<20% insulation difference) between the experimental Hi-C matrices of the two analyzed cell types were selected as structurally different genomic regions. In total, ~15% of the entire genome (~450 Mb) was included for evaluating the performance of cell-type-specific Hi-C prediction in each pair of cell types. In comparison, structurally conserved genomic regions were characterized by normal intensity (>10% intensity quantile) and high similarity (>20% insulation difference). These regions were used for control analysis in parallel with the aforementioned evaluation in the structurally differential genomic regions.56. The custom genome in HiC-bench is defined at the matrix-filtered step where the pipeline assigns reads to chromosomes. For the CUTLL1 experiment, we defined a custom chromosome chr7chr9, with chr7:0-142800000 as the starting chromosome and chr9:136500000-138394717 as the ending chromosome. CUTLL1 t translocation is heterozygous, leading to allele-specific complexity to its corresponding Hi-C matrix. Since only one allele is translocated, the experimental Hi-C data mapped to either the normal reference genome or the t translocated reference genome would be a mixture of chromatin interactions from both translocated and normal chromosomes. To align with this hybrid effect of Hi-C contact map, we first separately predicted three sets of Hi-C maps: t translocated chromosome, normal chromosome 7 and normal chromosome 9. The predicted Hi-C matrix at the t locus is an average of the predicted Hi-C maps of t translocation chromosome and a fused prediction map ranging from normal chr7 to the breakpoint chr7:142,797,952 and extending from chr9:136,502,817 to the rest of normal chr9. We did not count the interchromosomal interactions at these loci due to their much weaker intensity compared with the intrachromosomal interaction at the translocation site.To generate experimental Hi-C data, we defined a custom chromosome in HiC-bench42, the CTCF ChIP–seq and ATAC–seq inputs were processed using the same pipeline with mm10 as the assembly number. The original C.Origami model trained with IMR-90 dense input features was used for prediction. For genome-wide evaluation of predicting mouse chromatin organization, we adopted the same procedure from the ‘Performance comparison with previous methods’ section.For the mouse Patski cell-type prediction32, following the same pipeline for ChIP–seq and Hi-C. In total, three sets of data, with conditions: untreated, auxin-induced CTCF depletion and wash-off, are processed. Since this study did not measure ATAC–seq, the C.Origami model was re-trained using only DNA sequence and CTCF ChIP–seq on the untreated condition. The re-trained model was then used for predicting chromatin organization in the CTCF depletion (auxin treatment) and restoration (auxin wash-off) conditions. Genome-wide performance benchmark followed the same procedure from the ‘Performance comparison with previous methods’ section.We preprocessed CTCF ChIP–seq and Hi-C on mESCs from Nora et al.62. Instead of taking a single output, GRAM operates on a region r in the output space y and runs backpropagation on all pixels within r. GRAM on region r in network layer m is defined as follows:k and region r. Formally, Z is the number of activations in the layer and the quotient is the gradient at position i, j in the activation layer m with respect to output r. m. k at layer m. In this study, we choose r to be the full output space. During forward propagations, activation  to represent the whole genome and performed 1-kb-resolution screening on the selected chromosomes.p of a locus as the difference between maximum and minimum signal within the range of three bins including the locus. We then selected the top 1% of the total screened regions as a cutoff for impactful elements based on the peak score.To obtain the most impactful elements from the screening result, we designed a custom peak calling algorithm. We defined the peak score cis-elements, we compiled a set of genomic annotations including TAD boundary regions, enhancers, promoters, intragenic regions and intergenic regions. The boundary region was generated by calling TAD boundaries at 10-kb resolution with HiC-bench56, using its TopDom module and connecting adjacent TADs. To increase robustness of TAD boundary calling, we expanded the boundary width to 50 kb. The promoter region was defined as a 5.5-kb window, spanning 5 kb upstream and 500 bp downstream of gene transcription start site. Enhancers were defined by the H3K4me1 modification, which marks both active and inactive enhancers64. The H3K4me1 ChIP–seq peaks for IMR-90 were downloaded from ENOCDE with accession number ENCFF611UWF (https://www.encodeproject.org/files/ENCFF611UWF). To increase robustness, we expanded peaks to have at least 1-kb width.To annotate the in silico genetic screen-identified impactful We conducted an in silico genetic screen at a fixed 2-Mb window without centering the deletion element. We systematically removed segments of 8,192 bp, or 1 bin, from model inputs. To scan through the entire 2-Mb region, we performed 256 deletion experiments at each bin and calculated the prediction difference map before and after deletion. To maintain input shape, we appended 8,192 bp of empty input features to the end.65. Briefly, for each chromatin remodeling gene, we designed on average 6–8 single guide RNAs (sgRNAs), for a total of ∼2,500 sgRNAs. The sgRNA sequences were synthesized by Twist Bioscience, and cloned into a lentivirus-based sgRNA vector tagged with GFP (Addgene plasmid no. 65656). Cas9-expressing T-ALL cell lines were transduced with sgRNA library virus at a low multiplicity of infection , followed by infection efficiency assessment through GFP percentage on Day 4 posttransduction. Remaining cells were placed back into culture until 20 day posttransduction.Pooled CRISPR screenings across 313 chromatin remodeling genes in CUTLL1 and Jurkat cells were carried out in parallel with our previous studies for pooled screening of RNA binding protein in T-ALL cells65. For pooled CRISPR screening analysis, samples of each time-point were normalized as sgRNA read count/total read count × 100,000. Subsequently, normalized reads were then used to calculate log2fc  for each gRNA. The fold changes between Day 4 and Day 20 for each gene were averaged from all CRISPR gRNA targets. P values were calculated via a two-sided t-test comparing the fold changes of all gRNA targets of the same gene with fold change of 1.Cell proliferation was measured by comparing the sgRNA frequencies between Day 4 and Day 20 cells. Genomic DNA was collected from Day 4 and Day 20 cells using Qiagen DNA Purification kit based on the manufacturer’s protocol. The gRNA frequencies in the genomic DNA were amplified and quantified following our previous procedure56 was used to compute the interactions of each selected viewpoint in a roll-window fashion. We summed the valid read pairs in a 5-kb area centered at 100-bp bins that covered the area of ±2.5 Mb from the viewpoint . The interactions were normalized by the total number of valid pairs of the sample.HiC-bench ‘virtual4C’ pipelinecis-elements with trans-acting regulators, we compared the selected cell-type-specific impactful regions by a custom peak calling method, followed by a transcription factor enrichment test for identifying potential trans-acting regulators. We used the transcription factor database from ReMap2022  package66. For common transcription factors with hit counts larger than 20,000, we downsampled profiles to 20,000. We calculated the Q value with FDR correction based on the 612 transcription factor profiles tested and used odds ratio as the main metric to determine enrichment of each factor in impactful cis-elements.To connect the differential patterns of 022 ref. . To redutrans-acting regulator profiles between different cell types, we first normalized the odds ratio within each cell type. We performed k-means clustering of transcription factors based on their normalized odds ratios in CUTLL1, Jurkat and T cells. The k-means clustering was performed with standard Euclidean distance with six centroids. The clusters were further grouped and visualized using a heatmap.To compare the contributing T. A paired two-sided t-test was performed on each single interaction bin within each reference TAD between the two samples. We also calculated the difference between the average scores of all interaction intensities within such TADs and the TAD interaction log fold change. Finally, a multiple testing correction is performed by calculating the FDR on the total number of TAD pairs tested. The TAD interaction change for each t in T is calculated as follows:Iteratively corrected matrices were re-normalized by dividing each bin value by the sum of all the values in the same distance bin in the same chromosome . All the TADs identified in the control sample were used as the reference TADs to compute the intra-TAD activity changes. The set of reference TADs between the two samples, S1 (control) and S2 (treatment), were denoted as set We classified the reference TADs in terms of Loss, Gain or Stable intra-TAD changes by using the following thresholds: FDR < 0.01, absolute TAD interaction log fold change > 0.25 and absolute TAD interaction change > 0.1.Additional software used included the following: software: HiC-bench, Seq-N-slide, MACS2 (v.2.1.1), FitHiC (v.2.0.7); Python packages: Pytorch (v.1.9.0), Numpy (v.1.22.3), pybigwig (v.0.3.18), scikit-image (v.0.19.3); R packages: EnhancedVolcano (v.3.16).Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41587-022-01612-8.Supplementary InformationSupplementary Tables 1 and 2 and Figs. 1–20.Reporting SummarySupplementary Data 1Cell-type-specific predictions.


A test-based strategy against coronavirus disease 2019 (COVID-19) is one of the measures to assess the need for isolation and prevention of infection. However, testing with high sensitivity methods, such as quantitative RT-PCR, leads to unnecessary isolation, whereas the lateral flow antigen test shows low sensitivity and false negative results. The purpose of this study was to evaluate the performance of the LumiraDx SARS-CoV-2 Ag test (Lumira Ag), a rapid microfluidic immunofluorescence method, in assessing infectivity.This study was performed from March 2022 to July 2022. A pair of nasopharyngeal swab samples were obtained from each patient with mild COVID-19. One swab was used for Lumira Ag testing, and the other for quantitative RT-PCR testing and virus culture.A total of 84 patients were included in the study. Among them, PCR, Lumira Ag test, and virus culture indicated positivity for 82, 66, and 24 patients, respectively. When comparing the Lumira Ag test to virus culture, its sensitivity was 100.0% (24/24), specificity, 30.0% (18/60); positive predictive value, 36.3% (24/66); and negative predictive value (NPV), 100.0% (18/18). The positive sample for virus culture was observed until the ninth day from the onset of symptoms, while the Lumira Ag test was observed until day 11.The Lumira Ag test showed high sensitivity and NPV (100% each) compared to virus culture. A test-based strategy using the Lumira Ag test can effectively exclude COVID-19 infectiousness.The online version contains supplementary material available at 10.1186/s12879-023-08821-9. Due to the continuation of coronavirus disease 2019 (COVID-19) for three years, a uniformly required duration of quarantine for patients is being reconsidered. As an alternative to quarantine, a test-based strategy against COVID-19 is a widely accepted measure to assess the need for isolation and infection prevention –5, reinfHowever, nucleic acid amplification testing, including RT-PCR, has a high sensitivity for SARS-CoV-2 regardless of its viability, leading to continuous positive test results for several weeks , 7. AddiThe LumiraDx SARS-CoV-2 Ag test  is a rapid antigen test that offers a qualitative test result using a rapid microfluidic immunofluorescence method, which is available as point-of-care testing. Previous studies reported that the Lumira Ag test exhibited higher clinical sensitivity and specificity than RT-PCR . HoweverA prospective study was conducted from March–July 2022 to assess the performance of Lumira Ag in detecting SARS-CoV-2 infectivity. According to the surveillance data, the dominance of Omicron variants was greater than 99.67% during the study period . The stuLumira Ag testing was performed according to the instructions of the manufacturer. Briefly, the swab was placed into the extraction buffer, which was applied to the test strip inserted in the LumiraDx instrument. The qualitative result was obtained within 12 min.Quantitative RT-PCR testing was performed according to the National Institute for Infectious Diseases guidelines . NucleicThe virus was isolated to check infectivity using swab samples stored in VTM. A 100 µL aliquot of each VTM was inoculated in Vero E6 cells cultured in Eagle's medium supplemented with 2% fetal calf serum and 1% penicillin or streptomycin solution. Infected cells were cultured at 37 °C and observed for cytopathic effects daily. The infected culture fluid (ICF) was collected 5 days after infection for the first passage. The second passage of the viral culture was performed using a fresh monolayer of Vero E6 cells following the same procedure used in the first passage of the virus culture. Approximately 200 µL second passage of ICF was used for viral RNA extraction by the Nextractor NX-48 robot using the NX-48S Viral NA Kit (Genolution Inc.). The presence of the virus in ICF was verified using quantitative real-time RT-PCR. The cytopathic effect in viral culture was assessed and referred to as infectivity –16.To compare the viral load between paired samples and assess the Lumira Ag buffer and VTM as a matrix for the nucleic acid amplification test, the residual buffer from Lumira Ag testing was subsequently used for quantitative RT-PCR testing. The quantitated viral loads were expressed in terms of copies/test and compared on a scatter plot.P < 0.05.Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the Lumira Ag test to viral culture were calculated. The unpaired t-test was performed to compare the viral load between groups. The level of statistical significance was set at A total of 84 patients with COVID-19  who were between 2 and 13 days from the onset of symptoms were included in the study. Among them, 82 patients underwent RT-PCR tested positive and subsequently proceeded for further analysis. Lumira Ag showed positive results for 66 of the participants, and viral culture for 24 , specificity was 30% (18/60), positive predictive value was 36.3% (24/66); and negative predictive value was 100% (18/18). NEG, negative; POS, positive.P < 0.0001)  raises concerns that positive results of Lumira Ag testing should not be interpreted as evidence for infectivity. The sample with a positive result of Lumira Ag testing was observed on the 11Viral culture reflects the replication-competent viral particles contained in a clinical specimen and is accepted as a measure to assess infectiousness. Previous clinical studies have defined viral culture as an indicator of infectiousness , 30. HowThe limitations of this study include that the Lumira Ag testing and other tests were performed using different samples obtained from the same patients, which might include some variation in the viral load contained in the samples. To address this issue, the viral load quantitated using RT-PCR was compared between paired samples; a comparable viral load was observed with a correlation coefficient of 0.8136. Therefore, the equivalent quality of paired samples was verified, and the variation was limited.In conclusion, the Lumira Ag test was compared to viral culture, showing high sensitivity and NPV (100% each) relative to viral culture. These findings imply that the Lumira Ag test can be used as a point-of-care test in test-based strategies, allowing social activities to continue during the pandemic.Additional file 1. SARS-CoV-2 isolation from swab sample. (A) Vero E6 cell control and (B) Vero E6 cells inoculated with swab sample showing cytopathic effect (CPE) at 4 days post inoculation. Magnification is 10x for all images.Additional file 2. The results of Lumira Ag testing, viral culture, and RT-PCR testing shown in copies/test.Additional file 3. The viral load of the paired samples as determined by RT-PCR. The X-axis shows the viral load of the samples stored in virus transport media (VTM) used for RT-PCR and viral culture. The Y axis shows the viral load of the samples used for Lumira Ag testing. The viral load was expressed as copies/test. Y = 0.8174x-0.2559, the correlation coefficient was 0.8136.


In this paper, a novel ultra-wideband UWB antenna element with triple-band notches is proposed. The proposed UWB radiator element operates from 2.03 GHz up to 15.04 GHz with triple rejected bands at the WiMAX band (3.28–3.8 GHz), WLAN band (5.05–5.9 GHz), and X-band (7.78–8.51 GHz). In addition, the radiator supports the Bluetooth band (2.4–2.483 GHz). Three different techniques were utilized to obtain the triple-band notches. An alpha-shaped coupled line with a stub-loaded resonator (SLR) band stop filter was inserted along the main feeding line before the radiator to obtain a WiMAX band notch characteristic. Two identical U-shaped slots were etched on the proposed UWB radiator to achieve WLAN band notch characteristics with a very high degree of selectivity. Two identical metallic frames of an octagon-shaped electromagnetic band gap structure (EBG) were placed along the main feeding line to achieve the notch characteristic with X-band satellite communication with high sharpness edges. A novel UWB multiple-input multiple-output (MIMO) radiator is proposed. The proposed UWB-MIMO radiator was fabricated on FR-4 substrate material and measured. The isolation between every two adjacent ports was below −20 dB over the FCC-UWB spectrum and the Bluetooth band for the four MIMO antennas. The envelope correlation coefficient (ECC) between the proposed antennas in MIMO does not exceed 0.05. The diversity gains (DG) for all the radiators are greater than 9.98 dB. The communication link depending on ultra-wideband technology is a promising solution for numerous wireless networks, such as wireless body area networks (WBANs), Internet of Things (IoT) network systems, personal area networks (PAN), and remote sensing networks. The ultra-wideband communication network was selected because it uses a very high bandwidth radiator, which enables high-speed data transmission with low power consumption i.e., −44.2 dBm) . The UWB4.2 dBm .Since the UWB antenna uses an RF signal with low power and in the presence of some obstacles causing multipath fading, the multiple-input multiple-output (MIMO) network was introduced as a promising solution to solve this issue. Not only does the MIMO network system suppress multipath fading, but it also improves the network channel capacity . TherefoIn this paper, four ports of an UWB-MIMO structure were fabricated on a low-cost FR-4 substrate with relative permittivity of The article is arranged as follows. The design of a UWB antenna element is explained in h = 1.5 mm. The proposed UWB antenna element consists of three sections placed on the top layer of the dielectric substrate. The first one is a 50 C is the speed of light , one at the beginning of the WLAN band at a frequency of f = 4.94 GHz and the other one located at the end of the band at a frequency of f = 6.2 GHz. Thus, the WLAN band notch resembles a rectangular shape. This rectangular notch band improves the radiating characteristic of the antenna more than the conventional notch antenna .f = 7.29 GHz and f = 8.59 GHz, respectively. Adding the Bluetooth band to the UWB signal is very useful for establishing a communication channel between unlicensed bands for users/patients and diagnostic devices in biomedical applications.The final step in designing the proposed UWB antenna element with a triple band notch is to design a third band notch for X-band satellite communication that operates from 7.9 to 8.4 GHz. The X-band satellite communication band notch is required to be taken into account to overcome the interference between the obtained FCC-UWB signal and the X-band satellite signal. Electromagnetic band gap structure (EBG) technology is introduced to design a band stop filter to reject the licensed X-band satellite communication band over the aforementioned frequency range. A frame composed of an octagon-shaped EBG was proposed to achieve a band notch characteristic at X-band satellite communication in the UWB antenna, as shown in i is 1, 2, 3, and 4) at ports 1, 2, 3, and 4 of the UWB-MIMO antenna with the separate ground plane  configuration. It is shown that the FCC-UWB spectrum with triple band notches was obtained in addition to the Bluetooth band. On the other hand, i = 1, 2, 3, and 4) at ports 1, 2, 3, and 4 of the UWB-MIMO antenna with the common ground plane  configuration. It is noticed that the FCC-UWB spectrum with triple band notches was obtained but there is a mismatching characteristic at the Bluetooth band due to the coupling between the UWB antenna elements.Four proposed UWB antenna elements are perpendicularly placed to each other in order to design a MIMO structure, as shown in f = 2.44 GHz are shown in In order to overcome the problem mentioned above, a defective ground structure (DGS) band stop filter (BSF) was proposed and designed at the Bluetooth band . The two-port DGS-BSF was simulated in CST microwave studio, as shown in h = 1.5 mm. The proposed UWB-MIMO antenna was fabricated on FR-4 material with relative permittivity of i and port j, respectively, while i and port j.i  and port j . The suffixes i and j represent the port numbers. In the case of i = 1 and j = 2, the calculated i, respectively. j, respectively.ECC represents the envelope correlation coefficient between the radiated antennas in the MIMO configuration. There are two methods of calculating the ECC. One is obtained from the S-parameter characteristics using Equation , while tAnother important parameter in terms of diversity characteristics is the total active reflection coefficient (TARC). The total active reflection coefficient (TARC) parameter shows the active operating spectrum for the proposed MIMO radiator configuration. Equation  is used Finally, h = 1.5 mm. The UWB radiator element was proposed to operate in the FCC-UWB network. It has a bandwidth of 13.01 GHz extending from 2.03 to 15.04 GHz with triple band notches. The triple bands are the WiMAX band (3.28–3.8 GHz), WLAN band (5.05–5.9 GHz), and X-band (7.78–8.51 GHz). In addition to this, it supports the Bluetooth band (2.4–2.483 GHz). An alpha-shaped coupled line connected with a stub-loaded resonator (SLR) was proposed to achieve a band-stop characteristic at the WiMAX band. Two similar U-shaped slots etched on the radiator are utilized to obtain a band-stop characteristic WLAN band. Two unit cells of an octagon-shaped EBG structure are used to achieve a band-stop characteristic at X-band satellite communication. The three notch bands have excellent selectivity or sharpness at both the start and the end of each notch band. The UWB radiator is used in the MIMO configuration as an element. The ground planes of the four UWB radiators are connected together. Four DGS band-stop filters were placed between each ground plane and the center H-shaped ground connection to improve the matching characteristics at the Bluetooth band. In addition, high isolation characteristics were obtained at the operating band.Four ground-connected UWB-MIMO radiators were designed and fabricated on FR-4 dielectric substrate material with relative permittivity of


CUBN gene encoding the main albumin-transporter in the proximal tubule of the kidneys have previously been associated with microalbuminuria and higher urine albumin levels, also in diabetes. Sequencing studies in isolated proteinuria suggest that these variants might not affect kidney function, despite proteinuria. However, the relation of these CUBN missense variants to the estimated glomerular filtration rate (eGFR) is largely unexplored. We hereby broadly examine the associations between four CUBN missense variants and eGFRcreatinine in Europeans with Type 1 (T1D) and Type 2 Diabetes (T2D). Furthermore, we sought to deepen our understanding of these variants in a range of single- and aggregate- variant analyses of other kidney-related traits in individuals with and without diabetes mellitus.Rare genetic variants in the CUBN missense variants  and eGFRcreatinine , natural log-transformed) in populations with T1D  or T2D  from multiple European studies and in individuals without diabetes from UK Biobank  with replication in deCODE . Summary results of the diabetes-group were meta-analyzed using the fixed-effect inverse-variance method.We carried out a genetic association-based linear regression analysis between four creatinine and CUBN in the diabetes-group, we found significant positive associations between the minor alleles of all four variants and eGFRcreatinine in the UKBB individuals without diabetes with rs141640975 being the strongest . We replicated the findings for rs141640975 in the Icelandic non-diabetes population . For rs141640975, the eGFRcreatinine-association showed significant interaction with albuminuria levels . An aggregated genetic risk score (GRS) was associated with higher urine albumin levels and eGFRcreatinine. The rs141640975 variant was also associated with higher levels of eGFRcreatinine-cystatin C  and lower circulating cystatin C levels.Albeit we did not observe associations between eGFRCUBN missense variants and eGFR in a large population without diabetes suggests a pleiotropic role of CUBN as a novel eGFR-locus in addition to it being a known albuminuria-locus. Additional associations with diverse renal function measures (lower cystatin C and higher eGFRcreatinine-cystatin C levels) and a CUBN-focused GRS further suggests an important role of CUBN in the future personalization of chronic kidney disease management in people without diabetes.The positive associations between the four  In healthy individuals, the glomerular filter in the kidneys retains most of the albumin, although a small amount can usually pass through to the tubular system . ReabsorOver the past decades, the number of people with diabetes mellitus has more than doubled to a global prevalence of 537 million in 2021 , one in CUBN) – the main albumin-transporter in PTCs , rs144360241 , rs45551835 , and rs1801239 ); these are functional (missense) variants that have been proposed to alter the function of cubilin, leading to a form of albuminuria that may reflect a lack of tubular reabsorption of albumin   in the cubilin protein and to have different degrees of damaging effects on protein function, disease, or both ) in the gene encoding for cubilin , a clinically used marker of kidney function, is largely unexplored, and most genetic studies have focused on the general population  or type 2 diabetes mellitus (T2D). We then examined single- and aggregate-variant associations separately in diabetes and non-diabetes populations of a large, nationally representative cohort facilitating application of identical phenotype definitions, including the dependency of albuminuria-stage in SNV-eGFRcreatinine associations, generation of a CUBN-specific genetic risk score (GRS), and identification of associations between individual SNVs and cystatin C-based measures of kidney function. Together, these analyses both seek to replicate previous associations in DM and NDM populations and to provide novel insights into the link between CUBN and eGFR.However, the role of these pulation , 9, 11. subtypes . Thus fa22.1via three approaches , we included data collected proaches Figure 1proaches –18, Rottproaches , DiaGeneproaches , UK-ROI proaches , Genesisproaches  and ANDIproaches . These sSecond, we applied the same analysis plan to a subset of individuals with T2D  from the UK Biobank  hencefo, 26.Third, we did a lookup in a subset of an exome-wide association study (henceforth referred to as “ExWas”) that included 3,990 individuals with T2D from three Danish studies  described previously .www.type2diabetesgenetics.org, now: https://t2d.hugeamp.org/ (a) be readily available through the knowledge portal or a direct link to a study website; b) be available for diabetes-stratified and European-only populations; c) include at least one target genotype; d) be based on natural log-transformed eGFR values rather than non-transformed eGFR values; and e) be based on regression models with covariate adjustments comparable to those in the other cohorts in this study. However, as of 10 July 2020, no studies in the portal fulfilled our criteria, and no additional studies were included.We also searched the Type 2 Diabetes Knowledge Portal [at time of search: amp.org/ ] for lar1) a group of individuals without diabetes from UKBB , henceforth referred to as “UKBB-NDM”) and 2) the UKBB-T2D group, which was also part of the meta-analysis  with the Chronic Kidney Disease Epidemiology Collaboration creatinine equation (CKD-EPIcreatinine(2012), ml/min/1.73 m2 (For the DM cohorts and UKBB (both NDM and T2D groups), we calculated the creatinine-based estimated glomerular filtration rate  10, position (pos) 16992011 (genome-build GRCh37.p13)) with minor allele frequency (MAF) 0.002-0.009; rs144360241  with MAF 0.006-0.010; rs45551835  with MAF 0.016-0.021; and rs1801239  with MAF 0.097-0.114. For the deCODE study, the MAFs were in the same range except rs144360241 (MAF: 0.002). The minor alleles of these variants  were used as effect alleles.Four variants were selected for further analysis: 2< 0.1) between these SNVs.We used LDlink version 5.1  with theThe SNVs were first used in single-variant analyses and were then combined into a genetic risk score .2.4A flow chart of the meta-analyses is shown in 2.4.1creatinine and genetic variants were assessed assuming an additive genetic model. We used natural log-transformed eGFRcreatinine in a linear regression model (model 1) adjusted for traditional clinical and genetic factors, i.e. age, gender, and study-specific covariates . To control for potential bias on kidney function in the diabetes population, another model was further adjusted for HbA1C, systolic blood pressure (a proxy for medication with Angiotensin receptor blockers (ARBs) or Angiotensin-converting enzyme inhibitor (ACEi) frequently used in diabetes treatment) and diabetes duration (model 2). Some of the cohorts used summary statistics calculated prior to our query, so we allowed minor deviations in the included covariates (het< 0.05) indicated variation across studies. Effect sizes (betas) are presented with 95% confidence intervals. We evaluate statistical significance at an FDR-corrected level of 0.05/4 = 0.0125 considering the number of tested SNVs.In each DM cohort and UKBB-T2D, associations between eGFR2.4.2CUBN-variants and kidney-related traits in more detail, we did a range of additional linear regressions in the UKBB NDM and T2D groups. Further, we also applied a combined genetic risk score (GRS). We based the analyses on model 1 and model 3. The latter was very similar to model 2, in that it included adjustment for model 1 and SBP but not HbA1c and diabetes duration. The last two adjustments were absent from this model because they are less relevant in non-diabetes. We applied the same models in DM and NDM to provide consistency. Individuals were excluded if they had missing data for any variable.To explore the interplay between 2.4.2.1creatinine associations in the UKBB NDM and T2D populations. It was advantageous to use the UKBB dataset here as it is a well-powered, phenotypically homogenous dataset . Since effects are based on natural log-transformed eGFR (trait) values, we also calculated the percental difference in mean, non-transformed eGFR per added effect allele for significant effects as follows: % difference =(ebeta−1)*100%. . Again, we evaluated statistical significance at an FDR-corrected level of 0.0125.We examined SNV-eGFRcreatinine associations identified in the UKBB NDM group were also examined in the Icelandic deCODE study  applying model 3.SNV-eGFR2.4.2.2creatinine in an albuminuria-dependent fashion, we assessed albuminuria-SNV interactions in SNV-eGFRcreatinine regression models in individuals with T2D  and without DM  for whom continuous urine albumin levels were available . The interaction term in the regression models included albuminuria groups as a factor defined from these albumin levels as follows: i) normoalbuminuria: =< 30 mg/L , ii) microalbuminuria: 30-300 mg/L , and iii) macroalbuminuria: >300 mg/L . We used regression models based on model 1 and 3  ~ SNV + albuminuria group + age + sex + SNV*albuminuria group and model 3: model 1 + SBP). A significant p-value (< 0.05) for the SNV*albuminuria interaction term was considered evidence for interaction. Interaction analysis was done whenever primary SNV-eGFRcreatinine analyses were well-powered.In order to examine whether the SNVs associated with eGFR2.4.2.3CUBN missense SNVs. The GRS was generated for each study participant using the sum of individual SNV effect alleles in the UKBB dataset. We then examined the associations between GRSCUBN and continuous urine microalbumin levels (mg/L) and eGFRcreatinine.We estimated an albuminuria genetic risk score (GRS) using the four albuminuria-associated 2.4.2.4creatinine-cystatin C equation (We examined the associations between the study SNVs and 1) circulating serum Cystatin C levels (mg/L) and 2) the more recent eGFRequation  that use2.4.3post-hoc power for main SNV-eGFRcreatinine associations in DM and NDM groups.We used Quanto (version 1.2.4)  to calcua) chose a continuous design for independent individuals; b) assumed a gene-only hypothesis; c) assumed an additive inheritance mode; and d) set the two-sided type I error-rate to 0.05.For all power calculations in Quanto, we: creatinine association analyses (main effect). Means and standard deviations of ln(eGFRcreatinine) were derived from UKBB subsets. Unless otherwise specified, total DM sample sizes were used.For the remaining options in Quanto, we typed in information specific to each variant and population 14: For 33.1Up to 34,743 individuals with diabetes mellitus (type 1 diabetes (T1D), n ~ 3,588, or type 2 diabetes (T2D), n ~ 31,155) and up to 370,061 without diabetes participated in the current study , 0.008 ± 0.002 (0.007 ± 0.002), corresponding to a difference of +0.8% (+0.7%) in mean eGFRcreatinine (ml/min/1.73 m2) for each additional copy of the affect allele, C. For rs45551835, the effect was 0.005 ± 0.001 (0.004 ± 0.001), corresponding to a difference of +0.5% (+0.4%) in mean eGFRcreatinine per copy of the A-allele. rs141640975 had the largest effect size, 0.02 ± 0.003 (0.02 ± 0.003), corresponding to a +2.02% (+2.02%) difference in mean eGFRcreatinine for each additional A-allele. The common variant, rs1801239, had the smallest effect size of 0.001 ± 0.0005, corresponding to a +0.1% difference in eGFRcreatinine for each C-allele. We replicated the finding that rs141640975 was significantly associated with higher eGFRcreatinine in non-diabetes in an Icelandic study  is depicted in In UKBB-NDM, we observed larger eGFRcreatinine, although the effects of three of the variants (except rs141640975) were in the same direction as in NDM  compared to those without diabetes (NDM) , rs45551835  and rs141640975 ) and one common variant ) on eGFRcreatinine levels separately in people with and without diabetes , including stratification for diabetes-type and supplemented by tests on circulating cystatin C levels, the recently updated eGFR-equation based on creatinine and cystatin C  . In contes (NDM)  performestatin C , and aggrs45551835 and higher eGFR-levels has been reported in a smaller type 2 diabetes (T2D) population from Denmark  or Angiotensin-converting enzyme inhibitor (ACEi) medication which is frequently used in diabetes treatment. As part of our sensitivity analyses, we included models adjusted for systolic blood pressure (a proxy for such medication) and did not find evidence that this could explain why no association was found in the diabetes group. Another reason could be the allele frequency of the variants may differ between Danish and UK populations. We need further validation in well-powered populations to confirm the relationship between the rs45551835 and eGFR in diabetes, especially in T2D. In case of a true lack of association, CUBN may be associated with higher levels of urine albumin  with replication in the Icelandic study , confirming what has previously been observed for this SNV in NDM (INT<P 0.05). Taken together with the already known associations of the minor alleles with higher albuminuria , shifting focus to non-diabetes populations. For all four V in NDM  – but aluminuria , this noisorders . FurtherCUBN has a benign or even slightly protective effect on kidney function in chronic kidney disease if glomerular albuminuria is also present (CUBN variants uncouple proteinuria from glomerular filtration barrier through declined cubilin expression accompanied by aberrant amnionless (AMN) localization in renal tubules. AMN is part of the receptor complex  necessary for tubular reabsorption of albumin. This is suggested to create a benign condition, not requiring any further proteinuria lowering treatment  combining the four variants – showed that a higher number of C-terminal CUBN risk alleles is associated with higher urine albumin and eGFRcreatinine levels and confirms both the single-variant association with higher urine albumin levels reported previously in diabetes and non-diabetes  equation which is based only on creatinine. Our results using the conventional eGFRcreatinine equation are concordant with our results from the updated equation in terms of directionality of effect and with our finding that rs141640975 is associated with lower cystatin C levels, which is another indicator of kidney function. It should be noted, though, that considering Finally, we examined the association between the study SNVs and two alternative markers of kidney function. In non-diabetes, the minor alleles of CUBN. The judicious use of UKBB leveraging individual-level genotype information to investigate interaction-analyses based on albuminuria groupings is a great strength of the current study, especially for rare variants.A strength of our study is the restriction to specifically diabetes- and non-diabetes-only subgroups so that effects from mixed diabetes-status are minimized. Heterogeneity is likely to be present in meta-analyses of a diverse set of cohorts originally used for different research purposes. Indeed, some of the cohorts included in our meta-analyses differ regarding available covariates and/or kidney disease status. However, we did not observe heterogeneity in our meta-analyses. In addition to this, we could minimize heterogeneity in the remainder of our analyses by using data from the UKBB, which is a nationally representative cohort facilitating application of identical phenotype definitions across subgroups. Another strength is the broad spectrum of additional analyses that we explored in the UKBB population to nuance our findings on the relationship between eGFR and CUBN-eGFR relationship in this population. Although we demonstrate that C-terminal missense variants in CUBN are associated with different measures of normal (or even higher) kidney function in non-diabetes, we emphasize that the current study is insufficient to establish causality. Finally, using multiple-testing-corrected significance thresholds might be too conservative when testing a very small number of variants from the same locus as it may remove true associations. In genome-wide studies, a conservative threshold of 5 × 10−8 is generally agreed upon for novel associations. There is less consensus on when and how to appropriately apply multiple testing correction in smaller-scale genetic studies dealing with a mixture of new and known associations. Nevertheless, we deemed that it would be fair to apply FDR-correction of the significance threshold to our primary analyses in DM and NDM.A major limitation is that we did not have sufficient statistical power for our meta-analyses in the diabetes group due to the limited availability of suitable datasets. Consequently, interpretations of T2D findings should not be overstated and we thus could not demonstrate, nor disprove, the presence of a CUBN on two facets of kidney function – albuminuria and eGFR – by reporting SNV-eGFR associations in a large study population without diabetes. The interaction between rs141640975 and albuminuria-status on eGFRcreatinine in this population and its associations with lower cystatin C and higher levels of eGFRcreatinine-cystatin C expands our knowledge of these variants in relation to measures of kidney function. The demonstration of a CUBN-focused GRS in relation to albuminuria and eGFRcreatinine further suggests an important role of CUBN-variants in the future personalization of chronic kidney disease management.In conclusion, the current study identifies the existence of pleiotropic genetic effects of The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethical approval has previously been obtained locally for each individual study. The patients/participants provided their written informed consent to participate in this study.NU, MS, PR, and TA contributed to conception and design of the study. NU wrote the first draft of the manuscript.NU, MC-G, CH, AN, AO, SS, D-AT, EA, MH, AM, EJS, MS, PR, and TA contributed to manuscript revision. D-AT, VS, KS, EA, MH, AM, PR and TA acquired data. NU, FA, MC-G, CH, AN, SS, LS, D-AT, and TA performed statistical analysis. NU, MC-G, LS, D-AT, AM, MS, PR, and TA contributed to interpretation of data. TA and MS acquired funding and TA administered this project. PR and TA supervised the project. LC and MG had other roles. All authors contributed to the article and approved the submitted version.


Oncogene 10.1038/s41388-021-02103-x, published online 10 November 2021Retraction to: The Editors-in-Chief have retracted this article because concerns have been raised regarding integrity of the images presented here. Specifically,There appears to be overlap between the top left and top right panels of Fig. 2E.There appears to be overlap between the top three panels of Fig. 2F. The bottom left and bottom right panels of Fig. 2F appear to be the same.C4-2 blot in Fig. 3E appears to be the same as blot presented as LNCaP-AI in Fig. 3I.There appears to be a partial overlap between two panels of Fig. 4J which are described as representing different experimental conditions.Part of Fig. 5E (GAPDH blot) appears to have previously been published as part of Fig. 5E in  by some The Editors-in-Chief therefore no longer have confidence in the reliability of the data presented in this article.Zhiqun Shang does not agree to this retraction. Boya Zhang has not explicitly stated whether they agree to this retraction. Mingpeng Zhang, Qi Li, Jianpeng Yu, and Yuanjie Niu have not responded to correspondence about this retraction. The Publisher has not been able to obtain a current email address for Yanjie Yang or Shimiao Zhu.


Secondary acute myeloid leukemia (S-AML) patients generally have a poor prognosis, but the chromosomal aberrations of S-AML have been rarely reported. We aimed to explore the chromosomal aberrations and clinical significance in patients with S-AML.i.e., at S-AML diagnosis).The clinical characteristics and karyotypes of 26 patients with S-AML were retrospectively analyzed. The overall survival (OS) was measured from the time of the patients’ transition to AML (P < 0.05).The study included 26 S-AML patients , with a median age of 63 years . They transformed from various hematologic malignancies or solid tumors; most of them were secondary to myelodysplastic syndrome (MDS). About 62% of the S-AML patients showed chromosomal aberrations. The serum lactate dehydrogenase (LDH) level in S-AML patients with abnormal karyotype was higher than those with normal karyotype. Apart from the differences in treatment regimens, S-AML patients with chromosomal aberrations had shorter OS (S-AML patients with abnormal karyotype have higher LDH levels and shorter OS than normal karyotype patients, and the OS of hypodiploidy was much shorter than hyperdiploid. Secondary acute myeloid leukemia (S-AML) refers to AML developing either after a prior hematologic disorder, usually myelodysplastic syndrome (MDS), myeloproliferative neoplasms (MPN), or MDS/MPN . CompareSome predictors of transformation have been identified, including mutations in genes involved in growth-signaling pathways  and mutations commonly seen in AML  . Recent Some major clinical features are poor prognostic factors for AML. For instance, high WBC and/or LDH levels were identified as significant predictive features for OS . Here, wA total of 26 S-AML participants diagnosed or treated in the Second Affiliated Hospital of Anhui Medical University from January 2009 to January 2020 were collected. All the newly diagnosed S-AML participants met the 2008 or 2016 WHO criteria . In the i.e., at S-AML diagnosis). All cytogenetic analyses were carried out in a standardized fashion at the Chromosome Laboratory, Department of Hematology, The Second Affiliated Hospital of Anhui Medical University. Bone marrow specimens were prepared by the short-term culture method and the G-banding method. Twenty (20) metaphase spreads were examined per patient, if available. The International System for Human Cytogenetic Nomenclature (ISCN) was used for karyotyping  counts, white blood cell (WBC) counts, platelet counts (PLT), lymphocyte counts (LYM), mononuclear cell counts (MO), neutrophil counts (NEUT), hemoglobin (Hb), hypersensitive c-reactive protein (Hs-CRP) and lactate dehydrogenase (LDH), using the fully automated hematology analyzer Sysmex XE-2100  and the fully automated biochemical analyzer AU5831 .Participants were followed till death, loss to follow-up, or the end of the study follow-up period on July 20, 2020. OS was calculated from the time of S-AML diagnosis to the date of death or last follow-up. Medical record retrieval and telephone follow-up were performed during the study period.t-test was used to test the differences between the two groups for quantitative and normally distributed variables; the Mann–Whitney U test was used for non-parametric variables. Kaplan–Meier survival curves were used to estimate OS. Statistical analyses were performed with the IBM SPSS 25.0 software. Results were considered significant at p < 0.05.The student’s Half (n =13/26) of these 26 S-AML participants were men. In addition to unclassified AML , M2 was the most common FAB subtype in 42.3% (n =11/26) of the participants, followed by M4 , M3 , M5 , M7 . As for the diagnosis prior to AML, 57.7% (n =15/26) of the participants were secondary to MDS ), 3.8% (n =1/26) of the participants were secondary to myelodysplastic-myeloproliferative neoplasms (MDS/MPN), 11.5% (n =3/36) of the participants were secondary to chronic myeloid leukemia (CML), and 7.7% (n =2/26) of the participants were secondary to chronic myeloid monocytic leukemia (CMML), primary myelofibrosis (PMF) and rectal cancer respectively. 3.8% (n =1/26) of the participants were secondary to gastric diffuse large B cell lymphoma. The basic characteristics of 26 S-AML participants was shown in The treatment of AML is complex, and multiple subentities of AML often require different therapies. In our study, many participants were treated with decitabine in combination with other regimens. Other optional regimens such as azacytidine, IA/IAG regimen, and intrathecal injection have also been used to treat participants, depending on the patient’s condition. The detailed therapeutic regimen of 26 S-AML participants is shown in In this study, a total of 16 participants had chromosome abnormalities, 50% (n =8/16) of the participants had chromosome 5 abnormalities, 43.8% (n =7/16) of the participants had chromosome 7-8 and 11-13 abnormalities, 37.5% (n =6/16) of the participants had chromosome 9 and 22 abnormalities. Chromosome 15 and 21 abnormalities were found in 31.3% (n =5/16) of the participants. The frequencies of the other types of chromosomes were relatively low. The detail of each chromosome abnormality was shown in P < 0.05). The scatter diagram for the LDH levels between the 2 groups is shown, which only contains 23 participants  (P = 0.038) (P > 0.05). However, the OS of chromosomes 5, 7, 9, 11, 12 and 22 was statistically different from that of the normal chromosome (P < 0.05).Except for participants who were lost to follow-up, there were two deaths in the normal karyotype group and 12 deaths in the abnormal group. The median OS of normal and abnormal karyotypes was 212 days and 162 days, respectively. In addition, the abnormal karyotype included seven hyperdiploid and five hypodiploid participants, and the median OS was 211 days and 62 days, respectively. The Kaplan–Meier (KM) survival curve results showed that the OS of S-AML participants with abnormal karyotypes was shorter than those with normal karyotypes (= 0.038) . Also, c= 0.038) . KM survS-AML is a heterogeneous disease; its incidence increases with age, but therapy remains a challenge . MyelodyChromosomal aberrations are associated with progression to S-AML and deserved further study. The purpose of this study was to analyze the chromosomal aberrations of S-AML participants and further explore the factors connected with the survival and prognosis of S-AML in combination with relevant laboratory examinations. Our results indicated that most S-AML participants had abnormal karyotypes, including autosomal and sex chromosome aberrations. Abnormal changes in autosomal karyotypes were more common in S-AML participants and were closely related to survival and prognosis. Studies have demonstrated an increased incidence of abnormalities on chromosomes 5 and 7 in patients with S-AML . In our Most AML patients with chromosome number abnormalities may manifest with an increase of 1–2 chromosomes (47–48 chromosomes), known as low hyperdiploid, or rare high hyperdiploidy (49–65 chromosomes), both of which are associated with poor outcome in AML ; Sandah. HolmfelIn addition to other factors affecting OS, such as various treatment regimens, our research found that karyotypes were closely related to S-AML participants’ survival; participants with abnormal karyotypes demonstrated inferior OS compared with those with normal karyotype. What is more, S-AML participants with hypodiploidy showed worse outcomes than those with hyperdiploidy.LDH not only plays a vital role in the early diagnosis and prognosis of many solid tumors but also plays a crucial role in evaluating the severity of leukemia patients . LDH pose.g., CAG , retinoic acid) results in a higher OS rate than decitabine alone (It is of great significance to choose the appropriate chemotherapy regimen to manage patients with AML effectively. In clinical practice, an individualized treatment regimen is often tailored to the patient’s tolerance and other specific conditions. In our study, many participants were treated with decitabine in combination with other regimens. Decitabine is a demethylation agent that is effective and safe in older patients with AML; its combination with other regimens (ne alone . It is tThere are some limitations to our study. Firstly, the abnormality of sex chromosomes may relate to the survival and prognosis of S-AML, but no definite conclusion could be drawn because of the small number of sex chromosome aberrations in our study. A small number of patients did not get 20 metaphase spread, and there may be chromosomal abnormalities that have not been found, leading to a certain bias in the results. Apart from this, the accurate information of all participants could not be obtained through telephone follow-up in this study, which may interfere with the experimental results. Additionally, this study is a single-center retrospective study; the number of included cases was relatively small, so further study expanding the sample size is needed to validate our results. Moreover, with the heterogeneity of the individualized treatment among AML participants, the treatment regimens could constitute an important source of limitation, which may have influenced the results.In conclusion, our research highlights chromosomes and LDH contributions to the poor prognosis of S-AML participants. Also, the abnormality of sex chromosomes may be associated with the survival and prognosis of S-AML participants. Understanding the multifactorial contributions will lead to more precise risk classification and treatment strategies. More factors related to the survival and prognosis of S-AML need to be explored, which may contribute to monitoring the progression of the disease, early diagnosis, and improved treatment.10.7717/peerj.15333/supp-1Data S1Click here for additional data file.


Intranasal drug delivery is convenient and provides a high bioavailability but requires the use of mucoadhesive nanocarriers. Chitosan is a well-established polymer for mucoadhesive applications but can suffer from poor cytocompatibility and stability upon administration. In this work, we present a method to obtain stable and cytocompatible crosslinked chitosan nanoparticles. We used 2,6-pyridinedicarboxylic acid as a biocompatible crosslinker and compared the obtained particles with those prepared by ionotropic gelation using sodium tripolyphosphate. Nanoparticles were tested to evaluate the size and the surface charge, as well as their stability in storage conditions (4 °C), at the nasal cavity temperature (32 °C), and at the body temperature (37 °C). The crosslinked chitosan nanoparticles showed a size around 150 nm and a surface charge of 10.3 mV ± 0.9 mV, both compatible with the intranasal drug administration. Size and surface charge parameters did not significantly vary over time, indicating the good stability of these nanoparticles. We finally tested their cytocompatibility in vitro using SHSY5Y human neuroblastoma and RPMI 2650 human nasal epithelial cells, with positive results. In conclusion, the proposed synthetic system shows an interesting potential as a drug carrier for intranasal delivery. Over the past few decades, several studies have indicated the nasal mucosa as a promising therapeutic pathway for drug delivery . In addiMucoadhesion is a fundamental aspect of intranasal drug delivery . MucoadhThe optimization of mucoadhesion-related properties can be successfully exploited for topical drug administration  and in bNPs), is a valid strategy for poorly mucoadhesive active principles [The maximization of the effective drug dose in brain targeting is fundamental, and the encapsulation of the drug in mucoadhesive vectors, such as polymeric nanoparticles  is a natural, biocompatible, and biodegradable polymer that is derived from chitin, which is found in the exoskeleton of crustaceans. Chitosan has a positive charge, which allows it to interact with the negatively charged nasal mucus, thereby improving the mucoadhesion of nanoparticles. For such properties, CHI is a widely used mucoadhesive polymer for nasal drug delivery of insulin, vaccines, pain medications, peptides, and drugs for neurological disorders [Mucoadhesive polymers are materials with intrinsic mucoadhesive properties . Fully sisorders . Severalisorders ,13,23. Hisorders . Interacisorders , are maiisorders . Additioisorders . Mucoadhisorders . Severalisorders . Trimethisorders  and thioisorders  are amonCHI-TPP-NPs); (2) crosslinking of CHI with 2,6-pyridinedicarboxylic acid (DA), a non-toxic dicarboxylic acid that can form ester bonds with the free amines of chitosan (CHI-DA-NPs). CHI-NPs were investigated at physicochemical and biological levels.The present paper reports the development of a novel synthetic scheme to obtain chitosan nanoparticles (CHI-NPs) with elevated mucoadhesion potential and improved cytocompatibility. NPs were obtained by two strategies: (1) ionotropic gelation of CHI by using sodium tripolyphosphate (CHI-NPs obtained by ionotropic gelation with sodium tripolyphosphate (TPP) (CHI-TPP-NPs) were prepared. CHI-TPP-NP size slightly depended on the concentration of the TPP solution used for the preparation, with the size increasing with an increase in the amount of TPP used. The final size values obtained were 177 nm ± 57 nm and 336 nm ± 94 nm for CHI-TPP-NPs-0.1 and CHI-TPP-NPs-1, respectively a. TPP coWe also developed chitosan crosslinked nanoparticles with 2,6-pyridinedicarboxylic acid (DA) (CHI-DA-NPs). The size of CHI-DA-NPs ranged from 105 nm ± 36 nm to 1015 nm ± 164 nm, with a dependence on the DA concentration (from 1.25 to 20 mg/mL) used for the synthesis c. At theStability tests were performed to evaluate changes in size or surface charge over time in storage conditions (4 °C), in working conditions at the nasal cavity temperature (32 °C), or in physiological in vivo conditions (37 °C). The size of CHI-TPP NPs, in all the considered temperature conditions, was highly variable over time a,c, withBefore starting the in vitro characterization, we evaluated how the size of our NPs varied in the presence of the cell culture medium. We used DLS to monitor the size upon the resuspension (0 h) and after 24 h of incubation in cell culture medium supplemented with FBS (10%) at 37 °C . The sizWe then investigated the safety profile of our formulations by measuring the NPs’ effect on cell proliferation and viability in two relevant cell models for eventual intranasal brain delivery, the human neuroblastoma SH cells and the human nasal epithelial RPMI 2650 cells.We treated cells with different concentrations of CHI-TPP-NPs  for 24 h. For the higher concentration treatments (2.5 and 1 mg/mL), some color changes in the culture medium (+phenol red) were observed during the preparation of the solutions, which suggested a slight modification of the pH. We quantified the pH of the NP solutions in the cell medium . For botp < 0.01–0.001 vs. control, Dunnett’s test; p < 0.001 vs. control, Dunnett’s test; p < 0.05/0.01 vs. control, Dunnett’s test; Overall, CHI-TPP-NPs-0.1 negatively impacted SH cell proliferation and viability at concentrations within 0.5 mg/mL  for 24 and 48 h, again on SHs and RPMI-2650 c,d. FirsFinally, we qualitatively checked the vitality status of cells treated with CHI-TPP-NPs and CHI-DA-NPs-1.25 e–h. CHI-Overall, CHI-TPP-NPs are not highly biocompatible in vitro with neuronal cells, while they show a moderate improvement in their viability profile with nasal cells, at concentrations below 1 mg/mL. The formulation with 1 mg/mL TPP improves the NP safety profile with SH neural cells, but not with RPMI-2650 nasal cells. Overall, CHI-TPP-NPs show significant cell toxicity effects (in particular on SHs), except for the lower concentration (0.25 mg/mL). On the other hand, CHI-DA-NPs-1.25 show an improved safety profile. CHI-DA-NPs-1.25 slightly reduce both neuronal and nasal cells’ proliferation at the higher doses (≥2 mg/mL) but with low effects on cell vitality: in fact, almost all neuronal and epithelial cells treated with all CHI-DA-NPs-1.25 treatments are vital, with normal morphological features.We developed a novel synthetic scheme to obtain CHI-NPs with improved cytocompatibility and mucoadhesion potential. CHI-NPs crosslinked by DA, a non-toxic dicarboxylic acid, showed optimal characteristics at physicochemical and safety levels: results indicated a better stability and a higher cytocompatibility with neuronal and nasal epithelial cells with respect to those obtained by ionotropic gelation with TPP.CHI-TPP-NPs were selected as a control because such a system is already known in the literature and because the ionotropic gelation of CHI is well established. We have detected an effect of TPP concentration on the formation of CHI-NPs by ionotropic gelation. Ionotropic gelation involves the interaction between ionic charges on the polymer macromolecules and free ions added to the solution used for nanoparticle preparation. In our case, we have free cationic amines on the CHI molecules and five anionic negative charges for each TPP molecule. The smaller diameter of CHI-TPP-NPs-0.1 with respect to that measured for CHI-TPP-NPs-1 can be attributed to the lower number of CHI macromolecules involved in the formation of a single nanoparticle, due to the lower number of anionic groups. A lower number of TPP molecules reduces the possibility to obtain high crosslinking degrees, and thus CHI macromolecules have high mobility and can form a large number of smaller particles. On the other hand, the surface charge of the obtained nanoparticles did not vary with the TPP concentration. From this result, we can conclude that the amount of TPP used in the preparation of the formulation CHI-TPP-NPs-0.1 is not sufficient to form stable nanoparticles for our aim. A further improvement was obtained by changing the chemical scheme for the formation of the CHI network. We have selected a small molecule to be used as a crosslinker, DA, and we have proposed a crosslinking reaction based on the EDCl/NHS chemistry.The same trends for size and z-potential were detected for CHI-DA-NPs. However, in the case of CHI-DA-NPs-1.25, measured values of z-potential (10.3 mV ± 0.9 mV) were significantly different from those measured for CHI-TPP-NPs . We can conclude that, in the concentration ranges considered in this work, crosslinking sites obtained with DA are significantly more abundant than those obtained in ionotropic gelation with TPP. However, high positive values of z-potential are generally related to poor cytocompatibility ; thus, aStability tests performed on CHI-TPP-NPs indicated that such particles were not stable. Samples showed very high fluctuations in size at all the temperatures tested.On the contrary, CHI-DA-NPs-1.25 confirmed the high stability of the prepared nanoparticles, indicating that both the size and the z-potential did not change over time. The stability of the system at 4 °C ensures that nanoparticles can be safely stored in a mild condition after their preparation without any loss of physical properties. Moreover, the stability at 32 °C and at 37 °C at early times indicates that the system could control the potential release of the cargo in vivo, avoiding drug leakage or massive release in correspondence with their disaggregation. The high stability of the system CHI-DA-NPs-1.25 is due to the covalent crosslinking, which is stable in an aqueous environment. Longer test times are needed to define the shelf life of the system in storage conditions and the degradation kinetics of the polymer after the chemical crosslinking.Here, we chose SH human neuroblastoma cells and RPMI-2650 human nasal epithelial cells to test our NPs in vitro in relevant cell models, in view of their future application as vectors for intranasal administration to the brain. SHs are in fact a valuable model for studying neuronal cell behavior ,32,33,34CHI-TPP-NPs-0.1 highly impair SH cell proliferation, while CHI-TPP-NPs-1 behave similarly but to a lower extent. Such low biocompatibility with neuronal cells for CHI-TPP-NPs can be due to various factors, such as an excess of positive charge given by the free amines of chitosan, which can make cell membranes unstable , or the On the other hand, CHI-DA-NPs-1.25 show an optimal safety profile in vitro. CHI-DA-NPs-1.25 slightly reduce both neuronal and nasal cell proliferation only at the higher doses (≥2 mg/mL) but with low effects on cell vitality. RPMI-2650 cells in particular showed standard spreading and morphological features also exposed to the higher doses of CHI-DA-NPs-1.25. Overall, CHI-DA-NPs-1.25 have good physicochemical and in vitro biological features for the proposed application as an intranasal delivery system: a dimensional range around 150 nm and a mild positive z-potential with mucoadhesive potential. In fact, small NPs (<200 nm) need to be uploaded inside cells, and biological barriers , as wellE. coli [We developed these NPs envisioning repetitive intranasal delivery applications. We therefore purposely avoided the presence of any toxic or potentially toxic chemical in the NP formulation from the beginning: we searched for an alternative crosslinking agent in the place of the well-established glutaraldehyde, which is unfortunately highly toxic for humans. We here exploited DA, a biologically derived dicarboxylic acid: DA is highly present in nature, produced as a secondary metabolite in the core of certain bacterial spores, is readily biodegradable (can be degraded of the simple pyridines) ,44, and E. coli . ImportaMedium-molecular-weight chitosan  and sodium tripolyphosphate  were used in the preparation of CHI-TPP-NPs; CHI, 2,6-pyridinedicarboxylic acid , N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride , and N-Hydroxysuccinimide  were used for the synthesis of CHI-DA-NPs. Distilled water (DI), ethanol , acetic acid (Sigma Aldrich), and phosphate buffer  were used as solvents.First, 8 mL of DI and 2 mL of TPP solution in water (0.1 mg/mL and 1.0 mg/mL) were added to a glass beaker and mildly stirred. Then, 1 mL of chitosan solution  was added dropwise. The mixture was stirred for 1 h, and then it was concentrated to 2 mL by using a rotary evaporator under a vacuum. The final nanoparticle dispersion was then dialyzed over PBS for 48 h to eliminate acetic acid and residual TPP (membrane cut-off 14 kDa). The final nanoparticle suspension was stored in a glass vial at 4 °C before use.v/v) was added to the reactor. The reaction was maintained under stirring for 30 min, and then the product was dialyzed for 48 h over PBS to restore the pH to a physiological value and to eliminate the residual EDCl, NHS, and DA. The final nanoparticle dispersion was stored in a glass vial at 4 °C before use.First, 10 mL of DA solution in water (DA concentrations ranged from 1.25 mg/mL to 20.0 mg/mL) was added to a single-neck round-bottom glass reactor and stirred. Then, EDCl and NHS were added to the reactor (0.02 mmol/mL each). The mixture was stirred for 10 min, and then a CHI solution . A 10 μL sample was diluted in 400 μL of citrate buffer at pH 5 and added to a quartz cuvette for the measure. Surface charge was measured by z-potential. A 10 μL sample was diluted in DI and added to a folded capillary cell for the measure. All the measures were performed in triplicate. Data are reported as average values ± the standard error of the mean (mean ± SEM); at least n = 2 formulations for each type of NPs.Nanoparticle stability was checked at 4 °C (storage condition), 32 °C , and 37n = 2 formulations for each type of NPs, with similar results.We also monitored the NP size upon resuspension (0 h) and after 24 h of incubation in cell culture medium (DMEM supplemented with FBS 10%) by DLS. NP stock solutions (5 mg/mL) were diluted in medium to a concentration of 2 mg/mL. The prepared suspension was further diluted 1:1 with medium supplemented with FBS 20% to obtain an NP concentration of 1 mg/mL and an FBS concentration of 10% in medium. Then, 60 µL of the solution was added to a quartz cuvette and measured by DLS, immediately or after 24 h at 37 °C. We here tested 2). Human nasal septum carcinoma RPMI 2650 (RPMI-2650) cells (ATCC CCL-30), kindly provided by Prof. F. Sonvico and A. Bianchera , were cultured in MEM medium supplemented with 10% FBS, 1% nonessential amino acids, 2 mM glutamine, 10 U/mL penicillin–10 mg/mL streptomycin, and 1 mM sodium pyruvate, as in [n = 3 formulations were measured for each NP type.Human neuroblastoma-derived SH-SY5Y (SH) cells (ATCC HTB-11) were cultured in DMEM medium supplemented with 10% FBS, 4 mM glutamine, and 10 U/mL penicillin–10 mg/mL streptomycin, in standard conditions , following the instructions (protocol for end-point format). The assay is based on the reduction of NanoLuc substrate from metabolically active cells. After incubation, the solution of each well was moved to a new well in a black plate, and the luminescence was read with a GloMax DISCOVER microplate reader (Promega). The results are reported as percentages of cells with respect to the untreated cells (control condition). We tested each condition in duplicate in the assay, for at least ti inverted wide-field fluorescence microscope  equipped with a 20× air Nikon objective , an incubating chamber , and a CCD ORCA R2 .Cell live/dead vitality measurements were carried out by the simultaneous imaging of live and dead cells, as in . Calceinp < 0.05.Cell proliferation data are reported as average values ± the standard error of the mean (mean ± SEM); only Ctrl is reported as mean ± SD, to show the intra-assay variability. Data were statistically analyzed using GraphPad PRISM program, version 5.00 . One-way ANOVA analysis (Dunnett’s test) was used to compare different conditions. Statistical significance refers to We developed CHI-DA-NPs with optimal physicochemical and biological features for future applications as an intranasal delivery system for the brain. These NPs have a dimensional range of around 150 nm, a mild positive z-potential , and an optimal biocompatibility profile in vitro with both human neuronal and nasal epithelial cells and are developed via a maximally safe synthesis process, with no yield of hazardous substances.